SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3225 of 7282 papers

TitleStatusHype
GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction—0
MRIR: Integrating Multimodal Insights for Diffusion-based Realistic Image Restoration—0
High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching—0
Generalized Robust Fundus Photography-based Vision Loss Estimation for High MyopiaCode0
Orthogonal Constrained Minimization with Tensor _2,p Regularization for HSI Denoising and Destriping—0
Oracle Bone Inscriptions Multi-modal Dataset—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling—0
Robust ADAS: Enhancing Robustness of Machine Learning-based Advanced Driver Assistance Systems for Adverse Weather—0
Latent Diffusion Model for Generating Ensembles of Climate Simulations—0
UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks—0
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly DetectionCode0
Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration—0
Diffusion Models for Tabular Data Imputation and Synthetic Data Generation—0
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration—0
Learning Robust 3D Representation from CLIP via Dual DenoisingCode0
First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1—0
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations—0
Posterior Sampling with Denoising Oracles via Tilted Transport—0
Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration—0
Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness—0
Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation—0
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix—0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic ForgettingCode0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified